We are utilizing data from the longitudinal study “Transitions in Adolescent Girls,” (Barendse et al., 2019) specifically focusing on data from baseline (Wave 1). This dataset includes 174 participants who were 10-13 at the baseline. Our variables of interest are emotion regulation strategy use and depressive symptoms. These were measured using the Emotion Regulation Questionnaire (ERQ) (Gross & John, 2003) and the Center for Epidemiological Studies Depression Scale for Children (CES- DC). (Weissman et al., 1980) These are continuous scales which provide total scores for reappraisal and suppression (ERQ) and depressive symptoms (CES-DC).
Adolescence is a pivotal period of development characterized by rapid physical, social, and emotional change (Guyer et al., 2016; Nelson et al., 2016) Early adolescence has been proposed as a particularly crucial period for the development of emotion regulation (Silvers, 2022), the processes by which individuals moderate and modulate their emotional experiences and expression (Gross, 1998; Gross, 2015). Additionally, adolescents face heightened vulnerability to the depressive symptoms (Kessler et al., 2007), with depression rates spiking between early and middle adolescence and even steeper rates for girls (Hankin et al., 2007).
Drawing upon the longitudinal TAG dataset, we sought to examine whether two particularly salient and well-studied emotion regulation strategies (reappraisal and suppression) were associated with depressive symptoms in early adolescent girls (age 10-13).
These findings will serve as a foundation for future studies investigating these relations across time utilizing all six waves of the data.
Research Questions:
We first had to load in four separate datasets to join which contained age, race, emotion regulation, and depressive symptoms:
# Demographic
age <- read_csv(here("data", "TAG_age_session_dates_4waves.csv"))
age <- clean_names(age)
race <- read_excel(here("data", "TAG_W1_Race_Ethnicity.xlsx"))
race <- clean_names(race)
# Emotion Regulation(ERQ)
erq <- read_csv(here("data", "ERQ_Wave1.csv"))
erq <- erq %>%
clean_names() %>%
distinct(tagid, .keep_all = TRUE)
# Adolescent Depression(CESDC)
cesdc <- read_csv(here("data", "CESDC_Wave1.csv"))
cesdc <- clean_names(cesdc)Race/ethnicity category
First we had to clean the race/ethnicity data by identifying subjects reported multiple racial categories and rename them to “Multi-racial”
race_clean %>%
count(w1_ethnicity) %>%
ggplot(aes(x = fct_reorder(w1_ethnicity, n), y = n, fill = w1_ethnicity)) +
geom_col(show.legend = FALSE) +
coord_flip() +
labs(
title = "Distribution of Race/Ethnicity in Sample",
x = "Race/Ethnicity",
y = "Number of Participants"
) +
theme_minimal(base_size = 13)Age
Each wave consisted of two sessions, so we had to calculate the average age between sessions:
df <- erq %>%
left_join(cesdc, by= 'tagid')
df <- df %>%
left_join(age, by = 'tagid')
df <- df %>%
left_join(race_clean, by = 'tagid') %>%
select(tagid, erq_reappraisal_total, erq_suppression_total, ces_dc_total_75perc, W1_mean_age, w1_ethnicity,
w1s1_date, w1s1_age, w1s2_date, w1s2_age, w2s1_date, w2s1_age, w2s2_date, w2s2_age,
w3s1_date, w3s1_age, w3s2_date, w3s2_age, w4s1_date, w4s1_age, w4s2_date, w4s2_age
)In order to tidy the data, we first had to rename each wave/session/date/age variable
df <- df %>%
rename(
wave1_session1_date = w1s1_date,
wave1_session1_age = w1s1_age,
wave1_session2_date = w1s2_date,
wave1_session2_age = w1s2_age,
wave2_session1_date = w2s1_date,
wave2_session1_age = w2s1_age,
wave2_session2_date = w2s2_date,
wave2_session2_age = w2s2_age,
wave3_session1_date = w3s1_date,
wave3_session1_age = w3s1_age,
wave3_session2_date = w3s2_date,
wave3_session2_age = w3s2_age,
wave4_session1_date = w4s1_date,
wave4_session1_age = w4s1_age,
wave4_session2_date = w4s2_date,
wave4_session2_age = w4s2_age
)# Tidy
df2 <- df %>%
mutate(across(starts_with("wave"), as.character))
df_long <- df2 %>%
pivot_longer(
cols = starts_with("wave"),
names_to = c("wave", "session", "info"),
names_sep = "_",
values_to = "value"
) %>%
mutate(
wave = parse_number(wave),
session = parse_number(session)
)
df_tidy <- df_long %>%
pivot_wider(
names_from = info,
values_from = value
)
# Filter
df_final <- df_tidy %>%
filter(wave == 1, session == 1)| Unique | Missing Pct. | Mean | SD | Min | Median | Max | Histogram | |
|---|---|---|---|---|---|---|---|---|
| Reappraisal | 31 | 2 | 27.4 | 6.6 | 6.0 | 28.0 | 41.0 | |
| Suppression | 24 | 2 | 13.7 | 5.2 | 5.0 | 13.0 | 28.0 | |
| Depression | 49 | 5 | 12.8 | 10.3 | 0.0 | 9.5 | 50.0 | |
| Age | 141 | 1 | 11.6 | 0.8 | 10.0 | 11.7 | 13.1 | |
| Ethnicity | N | % | ||||||
| a. Black/ African American | 8 | 0.7 | ||||||
| b. Hispanic/ Latino/ Chicano | 48 | 4.2 | ||||||
| c. Native American or Native Alaskan | 8 | 0.7 | ||||||
| d. White / Caucasian | 720 | 63.4 | ||||||
| e. Asian | 16 | 1.4 | ||||||
| g. Multi-racial | 248 | 21.8 | ||||||
| h. Other | 16 | 1.4 | ||||||
| j. Decline to respond | 8 | 0.7 | ||||||
| NA | 64 | 5.6 |
Is higher emotion reappraisal associated with lower depression symptoms in adolescent girls?
Is higher emotion suppression associated with higher depression symptoms in adolescent girls?
Is higher suppression use associated with higher depression symptoms when adjusting for reappraisal use?
Does age moderate the relation between reappraisal/suppression and depression symptoms in adolescent girls?
| Reappraisal | Suppression | |
|---|---|---|
| + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001 | ||
| Intercept | 22.805*** | 3.864*** |
| (1.306) | (0.800) | |
| Reappraisal (ERQ) | -0.362*** | |
| (0.046) | ||
| Suppression (ERQ) | 0.645*** | |
| (0.055) | ||
| Suppression + Reappraisal | Reappraisal × Age Interaction | Suppression × Age Interaction | |
|---|---|---|---|
| + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001 | |||
| Intercept | 13.733*** | 40.986* | 2.701 |
| (1.484) | (18.413) | (11.043) | |
| Reappraisal (ERQ) | -0.332*** | -1.688** | |
| (0.043) | (0.644) | ||
| Suppression (ERQ) | 0.592*** | 0.053 | |
| (0.055) | (0.743) | ||
| Reappraisal x Age | 0.116* | ||
| (0.056) | |||
| Suppression x Age | 0.049 | ||
| (0.064) | |||
This plot shows the relation between reappraisal and depressive symptoms
This plot shows the relation between suppression and depressive symptoms